Evaluation of hypotension prediction index software in patients undergoing orthotopic liver transplantation: retrospective observational study
Avaliação do software de índice de predição de hipotensão em pacientes submetidos a transplante ortotópico de fígado: estudo observacional retrospectivo
Jacek B. Cywinski, Yufei Li, Lusine Israelyan, Roshni Sreedharan, Silvia Perez-Protto, Kamal Maheshwari
Abstract
Background
Extreme hemodynamic changes, especially intraoperative hypotension (IOH), are common and often prolonged during Liver Transplant (LT) surgery and during initial hours of recovery. Hypotension Prediction Index (HPI) software is one of the tools which can help in proactive hemodynamic management. The accuracy of the advanced hemodynamic parameters such as Cardiac Output (CO) and Systemic Vascular Resistance (SVR) obtained from HPI software and prediction performance of the HPI in LT surgery remains unknown.
Methods
This was a retrospective observational study conducted in a tertiary academic center with a large liver transplant program. We enrolled 23 adult LT patients who received both Pulmonary Artery Catheter (PAC) and HPI software monitoring. Primarily, we evaluated agreement between PAC and HPI software measured CO and SVR. A priori, we defined a relative difference of less than 20% between measurements as an adequate agreement for a pair of measurements and estimated the Lin's Concordance Correlation Coefficient and Bland-Altman Limits of Agreement (LOA). Clinically acceptable LOA was defined as ± 1 L.min-1 for CO and ± 200 dynes s.cm-5 for SVR. Secondary outcome was the ability of the HPI to predict future hypotension, defined as Mean Arterial Pressure (MAP) less than 65 mmHg lasting at least one minute. We estimated sensitivity, positive predictive value, and time from alert to hypotensive events for HPI software.
Results
Overall, 125 pairs of CO and 122 pairs of SVR records were obtained from 23 patients. Based on our predefined criteria, only 42% (95% CI 30%, 55%) of CO records and 53% (95% CI 28%, 72%) of SVR records from HPI software were considered to agree with those from PAC. Across all patients, there were a total of 1860 HPI alerts (HPI ≥ 85) and 642 hypotensive events (MAP < 65 mmHg). Out of the 642 hypotensive events, 618 events were predicted by HPI alert with sensitivity of 0.96 (95% CI: 0.95). Many times, the HPI value remained above alert level and was followed by multiple hypotensive events. Thus, to evaluate PPV and time to hypotension metric, we considered only the first HPI alert followed by a hypotensive event (“true alerts”). The “true alert” was the first alert when there were several alerts before a hypotension. There were 614 “true alerts” and the PPV for HPI was 0.33 (95% CI 0.31, 0.35). The median time from HPI alert to hypotension was 3.3 [Q1, Q3: 1, 9.3] mins.
Conclusion
There was poor agreement between the pulmonary artery catheter and HPI software calculated advanced hemodynamic parameters (CO and SVR), in the patients undergoing LT surgery. HPI software had high sensitivity but poor specificity for hypotension prediction, resulting in a high burden of false alarms.
Keywords
Resumo
Introdução
Alterações hemodinâmicas extremas, especialmente hipotensão intraoperatória (IOH), são comuns e frequentemente prolongadas durante a cirurgia de transplante hepático (LT) e durante as primeiras horas de recuperação. O software Índice de Predição de Hipotensão (HPI) é uma das ferramentas que podem auxiliar no manejo hemodinâmico proativo. A precisão dos parâmetros hemodinâmicos avançados, como Débito Cardíaco (CO) e Resistência Vascular Sistêmica (SVR), obtidos pelo software HPI, e o desempenho da predição do HPI na cirurgia de LT permanecem desconhecidos.
Métodos
Este foi um estudo observacional retrospectivo conduzido em um centro acadêmico terciário com um grande programa de transplante de fígado. Recrutamos 23 pacientes adultos em LT que receberam Cateter de Artéria Pulmonar (PAC) e monitoramento do software HPI. Primeiramente, avaliamos a concordância entre o PAC e o software HPI medidos CO e SVR. A priori, definimos uma diferença relativa de menos de 20% entre as medições como uma concordância adequada para um par de medições e estimamos o Coeficiente de Correlação de Concordância de Lin e os Limites de Concordância (LOA) de Bland-Altman. LOA clinicamente aceitável foi definido como ± 1 L.min-1 para DC e ± 200 dinas s.cm-5 para SVR. O desfecho secundário foi a capacidade do HPI de prever hipotensão futura, definida como Pressão Arterial Média (MAP) menor que 65 mmHg com duração de pelo menos um minuto. Estimamos a sensibilidade, o valor preditivo positivo e o tempo entre o alerta e os eventos hipotensivos para o software HPI.
Resultados
No total, 125 pares de registros de CO e 122 pares de registros de SVR foram obtidos de 23 pacientes. Com base em nossos critérios predefinidos, apenas 42% (IC 95% 30%, 55%) dos registros de CO e 53% (IC 95% 28%, 72%) dos registros de SVR do software HPI foram considerados concordantes com os do PAC. Em todos os pacientes, houve um total de 1.860 alertas de HPI (HPI ≥ 85) e 642 eventos hipotensivos (MAP < 65 mmHg). Dos 642 eventos hipotensivos, 618 eventos foram previstos pelo alerta de HPI com sensibilidade de 0,96 (IC 95%: 0,95). Muitas vezes, o valor de HPI permaneceu acima do nível de alerta e foi seguido por múltiplos eventos hipotensivos. Assim, para avaliar a métrica PPV e tempo até a hipotensão, consideramos apenas o primeiro alerta de HPI seguido por um evento hipotensivo ("alertas verdadeiros"). O "alerta verdadeiro" foi o primeiro alerta quando houve vários alertas antes de uma hipotensão. Houve 614 "alertas verdadeiros" e o PPV para HPI foi de 0,33 (IC 95% 0,31, 0,35). A mediana do tempo entre o alerta de HPI e a hipotensão foi de 3,3 [Q1, Q3: 1, 9,3] minutos.
Conclusão
Houve baixa concordância entre o cateter de artéria pulmonar e os parâmetros hemodinâmicos avançados calculados pelo software HPI (CO e SVR) nos pacientes submetidos à cirurgia de transplante de fígado (LT). O software HPI apresentou alta sensibilidade, mas baixa especificidade para a previsão de hipotensão, resultando em uma alta carga de alarmes falsos.
Palavras-chave
References
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Submitted date:
11/11/2024
Accepted date:
01/12/2025
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